to protect data owner privacy in FL. We also use third-party cookies that help us analyze and understand how you use this website. Conference Management Toolkit - Login The 48th International Conference on Parallel Processing (ICPP 2019), (acceptance rate: 20%), accepted, Kyoto, Japan. Big data Journal (impact factor: 1.489), vo. the 56th Design Automation Conference (DAC 2019), accepted, (acceptance rate: 20%), Las Vegas, US, 2019. We are soliciting submissions of short papers in PDF format and formatted according to the Standard ACM Conference Proceedings Template. Submissions that do not meet the formatting requirements will be rejected without review. 963-971, Apr-May 2015. ), Learning with algebraic or combinatorial structure, Link analysis/prediction, node classification, graph classification, clustering for complex graph structures, Theoretical analysis of graph algorithms or models, Optimization methods for graphs/manifolds, Probabilistic and graphical models for structured data, Unsupervised graph/manifold embedding methods. This workshop wants to emphasize on the importance of integrative paradigms for solving the new wave of AI applications. 2085-2094, Aug 2016. December, 12-16, 2022. Mingxuan Ju, Wei Song, Shiyu Sun, Yanfang Ye, Yujie Fan, Shifu Hou, Kenneth Loparo, and Liang Zhao. 2020. Qiang Yang, Hong Kong University of Science and Technology/ WeBank, China, (qyang@cse.ust.hk ), Sin G. Teo, Institute for Infocomm Research, Singapore (teosg@i2r.a-star.edu.sg), Han Yu, Nanyang Technological University, Singapore (han.yu@ntu.edu.sg), Lixin Fan, WeBank, China (lixinfan@webank.com), Chao Jin, Institute for Infocomm Research, Singapore (jin_chao@i2r.a-star.edu.sg), Le Zhang, University of Electronic Science and Technology of China (zhangleuestc@gmail.com), Yang Liu, Tsinghua University, China (liuy03@air.tsinghua.edu.cn), Zengxiang Li, Digital Research Institute, ENN Group, China (lizengxiang@enn.cn), Workshop site:http://federated-learning.org/fl-aaai-2022/. 625-634, New Orleans, US, Dec 2017. AI for infrastructure management and congestion. The papers may consist of up to seven pages of technical content plus up to two additional pages for references. Junxiang Wang, Fuxun Yu, Xiang Chen, and Liang Zhao. What is the status of existing approaches in ensuring AI and Machine Learning (ML) safety, and what are the gaps? Liang Zhao, Olga Gkountouna, and Dieter Pfoser. Realizing the vision of Document Intelligence remains a research challenge that requires a multi-disciplinary perspective spanning not only natural language processing and understanding, but also computer vision, layout understanding, knowledge representation and reasoning, data mining, knowledge discovery, information retrieval, and more all of which have been profoundly impacted and advanced by deep learning in the last few years. 1-39, November 2016. https://doi.org/10. 10 (2014): e110206. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), (Acceptance Rate: 25.6%), to appear, 2022. Information-theoretic approaches provide a novel set of tools that can expand the scope of classical approaches to causal inference and discovery problems in a variety of applications. Submit to: Papers are required to submit to:https://easychair.org/conferences/?conf=dlg22. DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums. Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting.Thirty-third AAAI Conference on Artificial Intelligence (AAAI 2019), (acceptance rate: 16.2%), Hawaii, USA, Feb 2019, accepted. DOI:https://doi.org/10.1145/3339823. Aug 11, 2022: Get early access for registration at L Street Bridge, Washington DC Convention Center, from 4-6 pm, Saturday, August 13. Neurocomputing (Impact Factor: 5.719), accepted. However, these real-world applications typically translate to problem domains where it is extremely challenging to even obtain raw data, let alone annotated data. The AAAI author kit can be downloaded from:https://www.aaai.org/Publications/Templates/AuthorKit22.zip. It is a forum to bring attention towards collecting, measuring, managing, mining, and understanding multimodal disinformation, misinformation, and malinformation data from social media. Please note as per the KDD Call for Workshop Proposals: Note: Workshop papers will not be archived in the ACM Digital Library. Deep Classifier Cascades for Open World Recognition. In recent months/years, major global shifts have occurred across the globe triggered by the Covid pandemic. The post-launch session includes the invited talks, shared task winners presentations, and a panel discussion on the resources, findings, and upcoming challenges. Disentangled Dynamic Graph Deep Generation, SIAM International Conference on Data Mining (SDM 2021), (acceptance rate: 21.3%), accepted. KDD 2022 : 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Conference Series : Knowledge Discovery and Data Mining Link: https://kdd.org/kdd2022/ Call For Papers [Empty] Related Resources KDD 2023 29TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING . Submissions introducing interesting experimental phenomena and open problems of optimal transport and structured data modeling are welcome as well. This 1-day workshop will include a mixture of invited speakers, panels (including discussion with the audience), and presentations from authors of accepted submissions. We are interested in a broad range of topics, both foundational and applied. Negar Etemadyrad, Yuyang Gao, Qingzhe Li, Xiaojie Guo, Frank Krueger, Qixiang Lin, Deqiang Qiu, and Liang Zhao. Each accepted paper presentation will be allocated between 15 and 20 minutes. Deep Graph Spectral Evolution Networks for Graph Topological Evolution. In the financial services industry particularly, a large amount of financial analysts work requires knowledge discovery and extraction from different data sources, such as SEC filings and industry reports, etc., before they can conduct any analysis. We invite thought-provoking submissions and talks on a range of topics in these fields. A challenge is how to integrate people into the learning loop in a way that is transparent, efficient, and beneficial to the human-AI team as a whole, supporting different requirements and users with different levels of expertise. Yujie Fan, Yiming Zhang, Shifu Hou, Lingwei Chen, Yanfang Ye, Chuan Shi, Liang Zhao, Shouhuai Xu. Participants are welcomed to submit their system reports to be presented in the workshop. Microsoft's Conference Management Toolkit is a hosted academic conference management system. Yuyang Gao, Lingfei Wu, Houman Homayoun, and Liang Zhao. Papers will be peer-reviewed and selected for spotlight and/or poster presentation. Submissions of technical papers can be up to 7 pages excluding references and appendices. Position papers are welcome. How do metrics of capability and generality, and the trade-offs with performance affect safety? At least three research trends are informing insights in this field. It is well-known that deep learning techniques that were disruptive for Euclidean data such as images or sequence data such as text are not immediately applicable to graph-structured data. Each oral presentation will be allocated between 10-15 minutes, while the spotlight presentation will be 2 minute each. In the Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), (acceptance rate: 17.9%), accepted, Macao, China, Aug 2019. Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, and Yanfang Ye. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022) (Acceptance Rate: 14.99%), accepted, 2022. Our topics of interest span over prediction, planning, and decision problems for online marketplaces, including but not limited to. By the end of this century, the earths population is projected to increase by 45% with available arable land decreasing by 20% coupled with changes in what crops these arable lands can best support; this creates the urgent need to enhance agricultural productivity by 70% before 2050. The goal of this workshop is to focus on creating and refining AI-based approaches that (1) process personalized data, (2) help patients (and families) participate in the care process, (3) improve patient participation, (4) help physicians utilize this participation to provide high quality and efficient personalized care, and (5) connect patients with information beyond that available within their care setting. IEEE Transactions on Pattern Analysis and Machine Intelligence (Impact Factor: 24.31), accepted. Shuo Lei, Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu. "Knowledge-enhanced Neural Machine Reasoning: A Review." IEEE Transactions on Neural Networks and Learning Systems (TNNLS), (Impact Factor: 14.255), accepted. Sigcomm 2022! - Authors of accepted papers will be invited to participate. Continuous V&V and predictability of AI safety properties, Runtime monitoring and (self-)adaptation of AI safety, Accountability, responsibility and liability of AI-based systems, Avoiding negative side effects in AI-based systems, Role and effectiveness of oversight: corrigibility and interruptibility, Loss of values and the catastrophic forgetting problem, Confidence, self-esteem and the distributional shift problem, Safety of AGI systems and the role of generality, Self-explanation, self-criticism and the transparency problem, Regulating AI-based systems: safety standards and certification, Human-in-the-loop and the scalable oversight problem, Experiences in AI-based safety-critical systems, including industrial processes, health, automotive systems, robotics, critical infrastructures, among others. Advances in IML promise to make AIs more accessible and controllable, more compatible with the values of their human partners and more trustworthy. with other vehicles via vehicular communication systems (e.g., dedicated short range communication (DSRC), vehicular ad hoc networks (VANETs), long term evolution (LTE), and 5G/6G mobile networks) for cooperation. We invite participants to submit papers by the 12th of November, based on but not limited to, the following topics: RL in various formalisms: one-shot games, turn-based, and Markov games, partially-observable games, continuous games, cooperative games; deep RL in games; combining search and RL in games; inverse RL in games; foundations, theory, and game-theoretic algorithms for RL; opponent modeling; analyses of learning dynamics in games; evolutionary methods for RL in games; RL in games without the rules; search and planning; and online learning in games. New self-supervised proxy tasks or new approaches using self-supervised models in speech and audio processing. It is difficult to expose false claims before they create a lot of damage. Workshop registration is available to AAAI-22 technical registrants at a discounted rate, or separately to workshop only registrants. Using a social media account will simply make the application process easier: none of your activities on this site will be posted to your profile. The excellent papers will be recommended for publications in SCI or EI journals. Contrast Pattern Mining in Paired Multivariate Time Series of Controlled Driving Behavior Experiment. Generative Deep Learning for Macromolecular Structure and Dynamics, Current Opinion in Structural Biology, (impact factor: 7.108), Section on Theory and Simulation/Computational Methods 67: 170-177, 2021 accepted. The objective of this workshop is to discuss the winning submissions of the Submissions to the Amazon KDD Cup 2022 issingle-blind (author names and affiliations should be listed). The workshop welcomes the submission of work on, but not limited to, the following research directions. IEEE Transactions on Neural Networks and Learning Systems (Impact Factor: 14.255), accepted. Welcome to the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022), which will be held in Chengdu, China on May 16-19, 2022. Integration of probabilistic inference in training deep models. Finally, the workshop will welcome papers that describe the release of privacy-preserving benchmarks and data sets that can be used by the community to solve fundamental problems of interest, including in machine learning and optimization for health systems and urban networks, to mention but a few examples. We invite the submission of papers with 4-6 pages. Ting Hua, Feng Chen, Liang Zhao, Chang-Tien Lu, and Naren Ramakrishnan. We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. KDD 2022 is a dual-track conference that provides distinct programming in research and applied data science. The first AAAI Workshop on AI for Design and Manufacturing, ADAM, aims to bring together researchers from core AI/ML, design, manufacturing, scientific computing, and geometric modeling. For previous workshops held physically, each workshop attracts around 150~300 participants. This workshop aims to bring together researchers from industry and academia and from different disciplines in AI and surrounding areas to explore challenges and innovations in IML. The industry session will emphasize practical industrial product developments using GNNs. considered to be more practical and more related with real-world applications. We will end the workshop with a panel discussion by top researchers in the field. Performance characterization of AI algorithms and systems under bias and scarcity. Self-supervised learning (SSL) has shown great promise in problems involving natural language and vision modalities. What techniques and approaches can be used to detect and effectively manage similar scenarios in the future? Attendance is open to all registered participants. With this in mind, we welcome relevant contributions on the following (and related) topic areas: The submissions must be in PDF format, written in English, and formatted according to the AAAI camera-ready style. Three specific roles are part of this format: session chairs, presenters and paper discussants. The theme of the hack-a-thon will be decided before submission is closed and will be focused around finding creative solutions to novel problems in health. RecSys 2022 - Important Dates - RecSys 25-50 attendees including invited speakers and accepted papers. In our workshop, we specifically focus on the trustworthy issues in AI for healthcare, aiming to make clinical AI methods more reliable in real clinical settings and be willingly used by physicians. Short papers 10m presentation and 5m discussion. In nearly all applications, reliability, safety, and security of such systems is a critical consideration. We invite submissions of technical papers up to 7 pages excluding references and appendices. Balaraman Ravindran (Indian Institute of Technology Madras, India ravi@cse.iitm.ac.in), Balaraman Ravindran (Indian Institute of Technology Madras, India Primary contact (ravi@cse.iitm.ac.in), Kristian Kersting (TU Darmstadt, Germany, kersting@cs.tu-darmstadt.de), Sriraam Natarajan (Univ of Texas Dallas, USA, Sriraam.Natarajan@utdallas.edu), Ginestra Bianconi (Queen Mary University of London, UK, ginestra.bianconi@gmail.com), Philip S. Chodrow (University of California, Los Angeles, USA, phil@math.ucla.edu) Tarun Kumar (Indian Institute of Technology Madras, India, tkumar@cse.iitm.ac.in), Deepak Maurya (Purdue University, India, maurya@cse.iitm.ac.in), Shreya Goyal (Indian Institute of Technology Madras, India, Goyal.3@iitj.ac.in), Workshop URL:https://sites.google.com/view/gclr2022/. Virtual . Benchmarks to reliably evaluate attacks/defenses and measure the real progress of the field. Causality has received significant interest in ML in recent years in part due to its utility for generalization and robustness. GeoInformatica (impact factor: 2.392), 24, 443475 (2020). We cordially welcome researchers, practitioners, and students from academia and industry who are interested in understanding and discussing how data scarcity and bias can be addressed in AI to participate. These complex demands have brought profound implications and an explosion of interest for research into the topic of this workshop, namely building practical AI with efficient and robust deep learning models. Applications of causal inference and discovery in machine learning/deep learning motivated by information-theoretic approaches (e.g. The topics of interest include, but are not limited to: The papers will be presented in poster format and some will be selected for oral presentation. Yevgeniy Vorobeychik (Washington University in St. Louis), Bruno Sinopoli (Washington University in St. Louis), Jinghan Yang (Washington University in St. Louis), Bo Li (UIUC), Atul Prakash (University of Michigan), Supplemental Workshop site:https://jinghany.github.io/trase2022/. The thematic sessions will be structured into short pitches and a common panel slot to discuss both individual paper contributions and shared topic issues. Negar Etemadyrad, Qingzhe Li, Liang Zhao. Positive applications of adversarial ML, i.e., adversarial for good. Liang Zhao, Jieping Ye, Feng Chen, Chang-Tien Lu, Naren Ramakrishnan. At least one author of each accepted submission must register and present their paper at the workshop. "Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning." It will include multiple keynote speakers, invited talks, a panel discussion, and two poster sessions for the accepted papers. As Artificial Intelligence (AI) begins to impact our everyday lives, industry, government, and society with tangible consequences, it becomes increasingly important for a user to understand the reasons and models underlying an AI-enabled systems decisions and recommendations. This workshop will follow a dual-track format. Although textual data is prevalent in a large amount of finance-related business problems, we also encourage submissions of studies or applications pertinent to finance using other types of unstructured data such as financial transactions, sensors, mobile devices, satellites, social media, etc. Please note that foreign students must allow for 3 to 6 months to complete all the formalities required to study in Canada. This AAAI-22 workshop on AI for Decision Optimization (AI4DO) will explore how AI can be used to significantly simplify the creation of efficient production level optimization models, thereby enabling their much wider application and resulting business values.The desired outcome of this workshop is to drive forward research and seed collaborations in this area by bringing together machine learning and decision-making from the lens of both dynamic and static optimization models. The accepted papers will be posted on the workshop website and will not appear in the AAAI proceedings. Paper Submission:November 12, 2021, 11:59 pm (anywhere on earth) Author Notification: December 3, 2021Full conference:February 22 March 1, 2022Workshop:February 28 March 1, 2022. CVPR 11 deadline . In some programs, spots may be available after the deadlines. If the admission deadline for international applicants is past, we suggest that you choose another session to begin your studies. It will start with a 60-minute mini-tutorial covering the basics of RL in games, and will include 2-4 invited talks by prominent contributors to the field, paper presentations, a poster session, and will close with a discussion panel. This is especially the case for non-traditional online resources such as social networks, blogs, news feed, twitter posts, and online communities with the sheer size and ever-increasing growth and change rate of their data. 2022. Ferdinando Fioretto (Syracuse University), Emma Frejinger (Universit de Montral), Elias B. Khalil (University of Toronto), Pashootan Vaezipoor (University of Toronto). 2022. 2022. The trained models are intended to assign scores to novel utterances, assessing whether they are possible or likely utterances in the training language. Dynamic Activation of Clients and Parameters for Federated Learning over Heterogeneous Graphs. Submissions are limited to a maximum of four (4) pages, including all content and references, and must be in PDF format. With the rapid development of advanced techniques on the intersection between information theory and machine learning, such as neural network-based or matrix-based mutual information estimator, tighter generalization bounds by information theory, deep generative models and causal representation learning, information theoretic methods can provide new perspectives and methods to deep learning on the central issues of generalization, robustness, explainability, and offer new solutions to different deep learning related AI applications.This workshop aims to bring together both academic researchers and industrial practitioners to share visions on the intersection between information theory and deep learning, and their practical usages in different AI applications. In spite of substantial research focusing on discovery from news, web, and social media data, its applications to datasets in professional settings such as financial filings and government reports, still present huge challenges. Published March 4, 2023 4:51 a.m. PST. VDS will bring together domain scientists and methods researchers (including data mining, visualization, usability and HCI, data management, statistics, machine learning, and software engineering) to discuss common interests, talk about practical issues, and identify open research problems in visualization in data science. We welcome attendance from individuals who do not have something theyd like to submit but who are interested in RL4ED research. chess, checkers). The impact of robustness assurance on other AI ethics principles: RAISA will also explore aspects related to ethical AI that overlap and interact with robustness concerns, including security, fairness, privacy, and explainability. Ting Hua, Liang Zhao, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. Question answering on business documents. There is increasing evidence that enabling AI technology has the potential to aid in the aforementioned paradigm shift. This is a 1-day workshop involving talks by pioneer researchers from respective areas, poster presentations, and short talks of accepted papers. The study of complex graphs is a highly interdisciplinary field that aims to study complex systems by using mathematical models, physical laws, inference and learning algorithms, etc. Information theory has demonstrated great potential to solve the above challenges. The workshop will include several technical sessions, a virtual poster session where presenters can discuss their work, to further foster collaborations, multiple invited speakers covering crucial aspects for the practical deep learning in the wild, especially the efficient and robust deep learning, some tutorial talks, the challenge for efficient deep learning and solution presentations, and will conclude with a panel discussion.